Stacking Arrays in NumPy

NumPy provides functions to stack arrays along different axes, allowing for efficient array manipulations. The primary stacking functions are:

  • hstack()
  • vstack()
  • dstack()
  • column_stack()
  • row_stack()

1. hstack()

The hstack() function horizontally stacks arrays (along axis 1).

    import numpy as np
    
    arr1 = np.array([1, 2, 3])
    arr2 = np.array([4, 5, 6])
    hstacked_arr = np.hstack((arr1, arr2))
    print(hstacked_arr)

Output:

    [1 2 3 4 5 6]

2. vstack()

The vstack() function vertically stacks arrays (along axis 0).

    arr1 = np.array([1, 2, 3])
    arr2 = np.array([4, 5, 6])
    vstacked_arr = np.vstack((arr1, arr2))
    print(vstacked_arr)

Output:

    [[1 2 3]
     [4 5 6]]

3. dstack()

The dstack() function stacks arrays along the third dimension.

    arr1 = np.array([[1, 2], [3, 4]])
    arr2 = np.array([[5, 6], [7, 8]])
    dstacked_arr = np.dstack((arr1, arr2))
    print(dstacked_arr)

Output:

    [[[1 5]
      [2 6]]
    
     [[3 7]
      [4 8]]]

4. column_stack()

The column_stack() function stacks 1D arrays as columns into a 2D array.

    arr1 = np.array([1, 2, 3])
    arr2 = np.array([4, 5, 6])
    col_stacked_arr = np.column_stack((arr1, arr2))
    print(col_stacked_arr)

Output:

    [[1 4]
     [2 5]
     [3 6]]

5. row_stack()

The row_stack() function stacks 1D arrays as rows into a 2D array.

    arr1 = np.array([1, 2, 3])
    arr2 = np.array([4, 5, 6])
    row_stacked_arr = np.row_stack((arr1, arr2))
    print(row_stacked_arr)

Output:

    [[1 2 3]
     [4 5 6]]

Conclusion

Stacking arrays in NumPy helps in data manipulation and structuring. The hstack() function stacks arrays horizontally, vstack() stacks them vertically, dstack() stacks along the third dimension, while column_stack() and row_stack() provide alternative stacking methods for 1D arrays.




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